DESCRIPTION (provided by applicant): The design and manufacture of custom insoles using CAD/CAM has the potential to revolutionize the care of diabetic patients at risk for foot ulceration, thereby preventing amputations. Using this approach, individualization of orthotics could become much more precise, consistent & less expensive than utilizing the still usual handcrafting. Several companies have developed such systems & for each the accurate measurement of foot shape is critical. Existing approaches use sophisticated digitizing machines to capture foot geometry and CAD/CAM to create insoles. These systems are very costly, & a significant portion of this cost is due to the foot digitizing devices. The objective of this revised proposal is to develop and bring to market an inexpensive device, the Haptic Lens, to capture the three-dimensional shape of the plantar surface of the foot. The major strengths of the proposed design are high resolution, low cost, & simplicity, which will make it accessible for use in small clinics & offices. In phase I of this study we have developed a second generation prototype of the Haptic Lens, written software to acquire and process the data, and used optimization methods to show promising results for the accuracy and repeatability. In this proposed phase II we will refine this prototype and accompanying software, reduce its cost, and prepare it for market. Specifically, we will optimize membrane safety and stiffness, add internal calibration, robust packaging, subject stabilization devices, and applied load measurement. The software will be refined to add automatic calibration, operator preview, faster execution, automatic image processing, and output that can easily be integrated into the CAD-CAM footwear algorithms. Performance will be defined through accuracy tests on objects of known geometry & on human feet. We will also develop operator training protocols and web-based instructions. This project is a critical part of an overall effort at DIApedia to design an orthotic system for at-risk diabetic patients that is based on clear scientifically supported design algorithms.